Apparent Electrical Conductivity,Soil Properties and Spatial Covariance in the U.S. Southern High Plains

Site-specific soil and crop management will require rapid low-cost sensors that can generate position-referenced data that measure important soil properties that impact crop yields. Apparent electrical conductivity (EC_a) is one such measure. Our main objective was to determine which commonly measured surface soil properties were related to EC_a at six sites in the Texas Southern High Plains, USA. We used the Veris 3100 and Geonics EM-38 EC mapping systems on 12 to 47 ha areas in six center-pivot irrigation sites. Soil samples were taken from 0–150 mm on a 0.1 to 0.8 ha grid and analyzed for routine nutrients and particle size distribution. At four of the six sites, shallow EC_a measured with the Veris 3100 (EC_a-sh) positively correlated to clay content. Clay content was negatively related with EC_a-sh at one site, possibly due to low bulk density of the shallow calcic horizon at that site. Other soil properties that were often correlated with EC_a included soil extractable Ca²⁺, Mg²⁺, Na⁺, CEC, silt and soluble salts. Extractable K⁺, NO₃^–, SO₄^–, Mehlich-3-P, and pH were not related to EC_a. Partial least squares regression (PLS) of seven soil properties explained an average of 61%, 51% and 37% of the variation in observed shallow EC_a-sh, deep EC_a with the Veris 3100 (EC_a-dp) and EC_a with the Geonics EM-38 (EC_a-em), respectively. Including nugget, range and sill parameters from a spherical semivariance model of the residuals from PLS regression improved the fit of mixed models in 15 of 18 cases. Apparent EC, therefore can provide useful information to land-users about key soil properties such as clay content and extractable Ca²⁺, but that spatial covariance in these relationships should not be ignored.     

Soil electrical conductivity as a function of soil water content and implications for soil mapping

Apparent soil electrical conductivity (EC_a) has shown promise as a soil survey tool in the Midwestern United States, with a share of this interest coming from the precision agriculture community. To fully utilize the potential of EC_a to map soils, a better understanding of temporal changes in EC_a is needed. Therefore, this study was undertaken to compare temporal changes in soil EC_a between different soils, to investigate the influence of changes in soil water content on soil EC_a, and to explore the impacts these EC_a changes might have on soil mapping applications. To this end, a 90 m long transect was established. Soil EC_a readings were taken in the vertical and horizontal dipoles at five points once every one to two weeks from June until October in 1999 and 2000. At the same time, soil samples were collected to a depth of 0.9 m for volumetric soil water content analysis. Soil EC_a readings were compared to soil water content. At four of the five sites linear regression analysis yielded r ² values of 0.70 or higher. Regression line slopes tended to be greater in lower landscape positions indicating greater EC_a changes with a given change in soil water content. Two of the soils had an EC_a relationship that changed as the soils became dry. This is an item of concern if EC_a is to be used in soil mapping. Results indicated that soil water content has a strong influence on the EC_a of these soils, and that EC_a has its greatest potential to differentiate between soils when the soils are moist. Soil water content is an important variable to know when conducting EC_a surveys and should be recorded as a part of any report on EC_a studies.     

Delineating productivity zones in a citrus grove using citrus production,tree growth and temporally stable soil data

The productivity of a citrus grove with variation in tree growth was mapped to delineate zones of productivity based on several indicator properties. These properties were fruit yield, ultrasonically measured tree canopy volume, normalized difference vegetation index (NDVI), elevation and apparent electrical conductivity (EC_a). The spatial patterns of soil series, soil color and EC_a, and their correspondence with the variation in yield emphasized the importance of variation in the soil in differentiating the productivity of the grove. Citrus fruit yield was positively correlated with canopy volume, NDVI and EC_a, and yield was negatively correlated with elevation. Although all the properties were strongly correlated with yield and were able to explain the productivity of the grove, citrus tree canopy volume was most strongly correlated (r = 0.85) with yield, explaining 73% of its variation. Tree canopy volume was used to classify the citrus grove into five productivity zones termed as ‘very poor’, ‘poor’, ‘medium’, ‘good’ and ‘very good’ zones. The study showed that productivity of citrus groves can be mapped using various attributes that directly or indirectly affect citrus production. The productivity zones identified could be used successfully to plan soil sampling and characterize soil variation in new fields.     

Imaging the electrical conductivity of the soil profile and its relationships to soil water patterns and drainage characteristics

Soil water content (θ) measurement is vital for accurate irrigation scheduling. Electromagnetic induction surveys can be used to map spatial variability of θ when other soil properties are uniform. However, depth-specific θ variations, essential for precision irrigation management, have been less investigated using this method. A quasi-2-dimensional inversion model, capable of inverting apparent soil electrical conductivity (EC_a) data to calculate estimates of true electrical conductivity (σ) down the entire soil profile, was developed using EC_a data collected by a multi-coil Dualem-421S sensor. The optimal relationships between σ and volumetric water content (θv) were established using all coil arrays of the Dualem-421S, a damping factor of 0.04, an initial model of 35 mSm^?1, and with ten iterations (R²?=?0.70, bias?=?0.00 cm³cm^?3, RMSE?=?0.04 cm³cm^?3). These relationships were then used to derive soil profile images of these properties, and as expected, θv and σ follow similar trends down the soil profile. The derived soil profile images for θv have potential use for irrigation scheduling to two EC_a-derived soil management zones under a variable rate irrigation system at this case study site. They reflect the intrinsic soil differences that occur between texture, texture transitions and drainage characteristics. The method can also be used to guide placement of soil moisture sensors for in-season monitoring of spatio-temporal variations of θv. This soil imaging method showed good potential for predicting 2D depth profiles of soil texture, moisture and drainage characteristics, and supporting soil, plant and irrigation management.

     

Development and validation of fuzzy logic inference to determine optimum rates of N for corn on the basis of field and crop features

A fuzzy inference system (FIS) was developed to generate recommendations for spatially variable applications of N fertilizer. Key soil and plant properties were identified based on experiments with rates ranging from 0 to 250 kg N ha⁻¹ conducted over three seasons (2005, 2006 and 2007) on fields with contrasting apparent soil electrical conductivity (EC_a), elevation (ELE) and slope (SLP) features. Mid-season growth was assessed from remotely sensed imagery at 1-m² resolution. Optimization of N rate by the FIS was defined against maximum corn growth in the weeks following in-season N application. The best mid-season growth was in areas of low EC_a, high ELE and low SLP. Under favourable soil conditions, maximum mid-season growth was obtained with low in-season N. Responses to N fertilizer application were better where soil conditions were naturally unfavourable to growth. The N sufficiency index (NSI) was used to judge plant N status just prior to in-season N application. Expert knowledge was formalized as a set of rules involving EC_a, ELE, SLP and NSI levels to deliver economically optimal N rates (EONRs). The resulting FIS was tested on an independent set of data (2008). A simulation revealed that using the FIS would have led to an average N saving of 41 kg N ha⁻¹ compared to the recommended uniform rate of 170 kg N ha⁻¹, without a loss of yield. The FIS therefore appears to be useful for incorporating expert knowledge into spatially variable N recommendations.     

Evaluation of a Geonics EM31-3RT probe to delineate hydrologic regimes in a tile-drained field

A world-wide need to use water resources efficiently necessitates more effective approaches to study water and contaminant transport in soil. This study examined the effectiveness of a multi-receiver electromagnetic induction probe (Geonics EM31-3RT) and modeling software (EMIGMA) to delineate hydrological regimes at field scale. The site consisted of 20 (15 m × 15 m) tile-drained plots in Southern Ontario, Canada. Measurements of apparent soil electrical conductivity (EC_a) and magnetic susceptibility were obtained using the EM31-3RT in each plot at four distances (0, 2.25, 4.5 and 7.5 m) from the tile drain, and on three occasions (August 22, 26 and 29) in 2003. The EMIGMA was used to simulate a depth profile of electrical conductivity (EC_s) from EM31-3RT readings. The near-surface soil showed significantly (p < 0.01) smaller EC_a values than at greater depth. The EC_a measurements made directly over the tile drains were smaller than those observed further away due to the presence of the drains. Cluster analysis indicated that the largest EC_a values were at the lower elevations of the site related to the redistribution of moisture from higher elevations. The effect of tile drains and rainfall events on EC_a was simulated well by EMIGMA, with smaller EC_s values above the drains compared to further away, and showing an increase in EC_s in the near-surface soil after rain. This study suggests that EM31-3RT measurements combined with EMIGMA simulation of electrical conductivity can provide valuable information on depth profiles of EC_a and water dynamics in soil.     

Linking Microbial-Scale Findings to Farm-Scale Outcomes in a Dryland Cropping System

Soil biological response to management is best evaluated in field-scale experiments within the context of the soil environment and crop; however, cost-effective methods are lacking to relate these data which span multiple spatial scales. We hypothesized that zones of apparent electrical conductivity (EC_a) could be used to integrate soil properties (sampling-site scale), microbial-scale measures of vesicular-arbuscular mycorrhizal (VAM) fungi, and field-scale wheat yields from yield maps. An on-farm dryland experiment (250 ha) was established wherein two (32-ha) fields were assigned to each phase of a winter wheat (Triticum aestivum L.) – corn (Zea mays L.) – proso millet (Panicum miliaceum L.) – fallow rotation. Each field was mapped and classified into four zones (ranges) of EC_a. Soil samples were collected from geo-referenced sites within EC_a zones and analyzed for multiple soil properties associated with productivity (0–7.5 and/or 0–30 cm). Additionally, VAM fungi were assessed using C16:1(cis)11 fatty acid methyl ester biomarker (C16_vam), glomalin immunoassay, and wet-aggregate stability (WAS) techniques (1–2mm aggregates from 0- to 7.5-cm soil samples). Concentrations of C16_vam and WAS increased among cropping treatments as: fallow < wheat < corn < millet. Glomalin across crops and replicates, C16_vam and WAS in fallow (crop effect removed), soil properties associated with productivity, and wheat yields were negatively correlated with EC_a and different among EC_a zones (P 0.05). Zones of EC_a provide a point of reference for relating data collected at different scales. Monitoring cropping system parameters and profitability, over time, may allow linkage of microbial-scale processes to farm-scale economic and ecological outcomes.     

Comparing apparent electrical conductivity measurements on a paddy field under flooded and drained conditions

Every growing season, paddy fields are kept both flooded and drained for a significant period of time. As a consequence, these soils develop distinct physico-chemical characteristics. For practical reasons, these soils are mostly sampled under dry conditions, but the question arises how representative the results are for the wet growing conditions. Therefore, the apparent electrical conductivity (EC_a) of a 1.4 ha alluvial paddy field located in the Brahmaputra floodplain of Bangladesh was measured in both dry and wet conditions by a sensing system using the electromagnetic induction sensor EM38, which does not require physical contact with the soil, and compared both surveys. Due to the smooth water surface under wet conditions which ensured increased stability of the sensing platform, the results of the survey showed considerably reduced micro-scale variability of EC_a. Furthermore, the wet survey results more reliably furnished soil-related information mainly due to the absence of soil moisture dynamics. The differences between EC_a under wet and dry conditions were attributed to differences in soil texture, mainly the sand content variation having considerable effect on soil moisture differences when flooded following drainage. Accordingly, the largest differences between EC_a under wet and dry conditions were found in those parts of the field with a large sand content. Hence, the conclusion was that an EC_a survey on flooded fields has an added value to precision soil management.     

Effect of Daily Soil Temperature Fluctuations on Soil Electrical Conductivity as Measured with the Geonics® EM-38

Soil electrical conductivity (EC_a) measured by electromagnetic induction (EM) using the EM-38 has shown promise as a soil survey tool. Soil temperature influences EC_a readings, and temperature can fluctuate considerably in the upper 10cm of the soil during a day. EC_a readings were taken in the horizontal and vertical dipole orientations once an hour from 8a.m. to 8p.m. at four sites on three separate days to determine if EC_a values were influenced by diurnal temperature variations. Soil temperature readings were taken at the same times at four depths. EM-38 readings remained steady at all four sites all 3days. Linear regression analysis when temperature in the upper 10cm was plotted against EC_a yielded low r ² values and slopes, indicating no correlation between soil temperature in the upper 10cm and EC_a values. Diurnal changes in soil temperature do not significantly influence soil EC_a readings obtained with the EM-38 under the conditions encountered during the study.     

A floating sensing system to evaluate soil and crop variability within flooded paddy rice fields

Continuous paddy rice cultivation requires fields to be flooded most of the time limiting seriously the collection of detailed soil information. So far, no appropriate soil sensor technology for identifying soil variability of flooded fields has been reported. Therefore, the primary objective was the development of a sensing system that can float, acquire and process detailed geo-referenced soil information within flooded fields. An additional objective was to determine whether the collected apparent electrical conductivity (EC_a) information could be used to support soil management at a within-field level. A floating sensing system (FloSSy) was built to record EC_a using the electromagnetic induction sensor EM38, which does not require physical contact with the soil. Its feasibility was tested in an alluvial paddy field of 2.7 ha located in the Brahmaputra floodplain of Bangladesh. The high-resolution (1 × 1 m) EC_a data were classified into three classes using the fuzzy k-means classification method. The variation among the classes could be attributed to differences in subsoil (0.15–0.30 m below soil surface) bulk density, with the smallest EC_a values representing the lowest bulk density. This effect was attributed to differences in compaction of the plough pan due to differential puddling. There was also a significant difference in rice yield among the EC_a classes, with the smallest EC_a values representing the lowest yield. It was concluded that the floating sensing system allowed the collection of relevant soil information, opening potential for precision agriculture practices in flooded crop fields.     

Spatial variability and temporal stability of apparent soil electrical conductivity in a Mediterranean pasture

The general objectives of this study were to evaluate (i) the specificity of the spatial and temporal dynamics of apparent soil electrical conductivity (EC_a) measured by a electromagnetic induction (EMI) sensor, over 7 years, in variable conditions (of soil moisture content (SMC), soil vegetation cover and grazing management) and, consequently, (ii) the potential for implementing site-specific management (SSM). The DUALEM 1S sensor was used to measure the EC_a in a 6 ha pasture experimental field four times between June 2007 and February of 2013. Soil spatial variability was characterized by 76 samples, geo-referenced with the global positioning system (GPS). The soil was characterized in terms of texture, moisture content, pH, organic matter content, nitrogen, phosphorus and potassium. This study shows a significant temporal stability of the EC_a patterns under several conditions, behavior that is an excellent indicator of reliability of this tool to survey spatial soil variability and to delineate potential site-specific management zones (SSMZ). Significant correlations were obtained in this work between the EC_a and relative field elevation, pH, silt and soil moisture content. These results open perspectives for using the EMI sensor as an indicator of SMC in irrigation management and of needs of limestone correction in Mediterranean pastures. However, it is interesting to extend the findings to other types of soil to verify the origin of the lack of correlation between the EC_a data measured by DUALEM sensor and properties such as the clay, organic matter or phosphorus soil content, fundamental parameters for establishment of pasture SSM projects.     

The measurement,prediction, and development of soil management zones in low-relief sodic soils

Inexpensive, accurate, and rapid measurements of sodicity are required to identify the restoration options for degraded sites. This study determined the spatial variability of the percent of ammonium acetate extractable Na (%Na), apparent electrical conductivity (EC_a), pH_1:1, elevation and topographic wetness index, and used this information to create %Na management zones. In an 8.1 ha North Dakota field that contained Natraquolls and Calciaquolls, 1088 soil samples from the 0–0.3 and 0.3–0.6 m were collected from a 12.2 by 12.2 m geo-referenced grid. At each grid point, the elevation and ECa was determined using a differential corrected global positioning system and EM38m, respectively. Soil samples were analyzed for the %Na, EC_1:1, pH_1:1, and soil dispersion. Exponential semi-variogram models explained 96.7% of the ln-transformed %Na data in the 0–0.3 m soil depth, and %Na was correlated to EC_1:1 (r = 0.54), pH_1:1 (r = 0.68), clay dispersion (r = 0.68), ECav (r = 0.49), and ECah (r = 0.57). Forward stepwise regression models based on elevation, EC_1:1, pH_1:1, and ECah explained 64 and 74% of the %Na variability in the surface 0.3 m and subsurface 0.3–0.6 m, respectively. Management zones were identified that reduced the %Na variability up to 82%.     

秸秆和植被覆盖对江苏滨海盐土土壤盐分变化的影响

研究秸秆覆盖和植被（田菁）覆盖条件下滨海盐土土壤水盐的动态变化规律,为沿海滩涂盐碱地脱盐改良提供依据。本研究以含盐量6.98 g·kg^-1的滨海盐土为研究对象,设置秸秆覆盖和种植田菁2种覆盖处理,以裸地为对照,研究不同覆盖处理对滨海盐土土壤含水量和土壤盐分动态变化的影响。结果表明：秸秆覆盖下的土壤含水量（27.58%）显著高于田菁覆盖（26.70%）和裸地（26.61%）,后两者差异未达显著水平。处理1年后0~20、20~40 cm土层的秸秆覆盖、田菁覆盖和裸地不同处理间土壤含盐量的差异均达显著或极显著水平;秸秆覆盖处理的脱盐率为田菁覆盖处理的2倍。回归分析表明裸地和田菁覆盖下土壤盐分含量与累积降雨量的关系可用二次多项式拟合,田菁覆盖下的淋洗方程（EC_a/EC_i与D_w/D_s间的关系）可用三次多项式拟合;而秸秆覆盖条件下土壤盐分含量与累积降雨量的关系、淋洗方程均表现为指数函数关系y=ae^bx（P<0.01）。秸秆覆盖条件下滨海滩涂0~40 cm土层脱盐80%需要386.8 mm的累积降雨量。结果表明秸秆和植被覆盖技术在江苏滨海盐土可获得较好的脱盐效果,具有较好的应用前景。     

黄淮海平原小麦吸收镉与土壤可浸提镉间关系研究

土壤重金属镉浸提量与小麦吸收镉量关系是评价浸提方法以及土壤中镉有效性的重要依据,为了给黄淮海平原小麦产区土壤镉污染评价提供科技支撑。分别采用0.43 mol·L~(-1)HNO_3、0.1 mol·L~(-1)HCl、0.05 mol·L~(-1)EDTA浸提土壤镉的方法对黄淮海平原11个不同地点大田土壤样品和24个小麦植株样品进行了测定,并对3种不同浸提剂浸提的土壤有效态镉量间的相互关系,及土壤浸提镉含量与小麦镉含量间的关系进行了探讨。结果表明:不同浸提剂浸提的土壤Cd含量、全量间的相关关系均达极显著水平;3种浸提剂的Cd浸提率为58.7%(0.43 mol·L~(-1)HNO_3)53.2%(0.05 mol·L~(-1)EDTA)25.9%(0.1 mol·L~(-1)HCl),不同浸提剂浸提的土壤有效态Cd含量与小麦植株Cd含量的相关性顺序为r=0.74(0.05 mol·L~(-1)EDTA)r=0.64(0.43 mol·L~(-1)HNO_3)r=0.49(0.1 mol·L~(-1)HCl);在pH为7.71~8.59和土壤全Cd含量范围为0.107~0.212 mg·kg~(-1)条件下,以EDTA浸提的土壤Cd含量及土壤Cd全量结合土壤pH值建立的方程可以较好地预测小麦籽粒Cd含量;经推算,EDTA浸提的土壤Cd和土壤Cd全量的安全临界值可分别为0.671 mg·kg~(-1)和1.02 mg·kg~(-1)。     

Apparent electrical conductivity in dry versus wet soil conditions in a shallow soil

The general objective of this study was to evaluate the stability of patterns of apparent soil electrical conductivity (EC_a) in dry versus wet soil conditions in a shallow soil typically used for pastures in Mediterranean conditions of the southern region of Portugal. A 6 ha experimental field of permanent bio-diverse pasture was divided into 76 squares of 28 × 28 m. The soil electrical conductivity was measured using a Dualem 1S sensor under dry conditions (June 2007) and under wet conditions during the rainy season (March 2010). Soil samples, geo-referenced with GPS, were collected in a depth range of 0–0.30 m. The soil was characterized in terms of bedrock depth, moisture content, texture, pH, organic matter content, and macronutrients (nitrogen, phosphorus, and potassium). Pasture samples, also geo-referenced with GPS, were collected to measure the pasture dry matter yield. The statistical analysis of apparent electrical conductivity between dry and wet soil conditions resulted in a linear significant correlation coefficient (R = 0.88). The results also showed a significant correlation between apparent electrical conductivity and the relative field elevation (R = ?0.64 and R = ?0.66), the pasture dry matter yield (R = 0.42 and R = 0.48), the bedrock depth (R = 0.40 and R = 0.27), the pH (R = 0.50 and R = 0.49), the silt (R = 0.27 and R = 0.38) and soil moisture content (R = 0.48 and R = 0.45), in dry and wet conditions, respectively. A multi-variate regression was carried out using the following soil parameters that showed significant correlation with EC_a and that did not present multi-collinearity: pH, bedrock depth, silt and moisture content. The results showed, in dry and wet conditions, that the analysis was significant (R = 0.75 and R = 0.84, respectively). Overall, these results indicate the temporal stability of EC_a patterns under different soil moisture contents, which is relevant with respect to the time when a field should be surveyed and is important for using the electrical conductivity sensor, as a decision support tool for management zones in precision agriculture.     

Spectral and thermal sensing for nitrogen and water status in rainfed and irrigated wheat environments

Variable-rate technologies and site-specific crop nutrient management require real-time spatial information about the potential for response to in-season crop management interventions. Thermal and spectral properties of canopies can provide relevant information for non-destructive measurement of crop water and nitrogen stresses. In previous studies, foliage temperature was successfully estimated from canopy-scale (mixed foliage and soil) temperatures and the multispectral Canopy Chlorophyll Content Index (CCCI) was effective in measuring canopy-scale N status in rainfed wheat (Triticum aestivum L.) systems in Horsham, Victoria, Australia. In the present study, results showed that under irrigated wheat systems in Maricopa, Arizona, USA, the theoretical derivation of foliage temperature unmixing produced relationships similar to those in Horsham. Derivation of the CCCI led to an r ² relationship with chlorophyll a of 0.53 after Zadoks stage 43. This was later than the relationship (r ² = 0.68) developed for Horsham after Zadoks stage 33 but early enough to be used for potential mid-season N fertilizer recommendations. Additionally, ground-based hyperspectral data estimated plant N (g kg⁻¹) in Horsham with an r ² = 0.86 but was confounded by water supply and N interactions. By combining canopy thermal and spectral properties, varying water and N status can potentially be identified eventually permitting targeted N applications to those parts of a field where N can be used most efficiently by the crop.     

土壤有机质对交换性酸测定结果的影响

为探讨土壤有机质对两种常用的土壤交换性酸测定方法(BaCl_2-TEA提取法和KCl淋溶法)结果的影响,选取了85个不同有机质质量分数的酸性土壤并采用BaCl_2-TEA提取法和KCl淋溶法测定其交换性酸含量.结果表明,BaCl_2-TEA提取法测得的土壤交换性酸含量远大于KCl淋溶法的测定结果.KCl淋溶法测得的土壤交换性酸含量与土壤pH值间的负相关性极有统计学意义(r=-0.79**),而BaCl_2-TEA提取法测得的土壤交换性酸含量与土壤pH值间的相关性无统计学意义(r=-0.08).但BaCl_2-TEA提取法测得的土壤交换性酸含量却与土壤有机质质量分数间的正相关性极有统计学意义(r=0.94**).此外,由KCl淋溶法测得的土壤交换性酸进一步计算得到的盐基饱和度与土壤pH值间的相关性(r=0.69**)也大于BaCl_2-TEA提取法(r=0.25*).通常土壤酸化越严重,土壤pH值越低,交换性酸含量越高,盐基饱和度越低.可以得出,土壤有机质会使BaCl_2-TEA提取法的测定结果产生较大的正误差,而对KCl淋溶法的影响较小.由于土壤有机质中的腐殖酸会与BaCl_2-TEA提取法中的有机弱碱TEA发生反应,增加TEA用量,从而使计算得到的土壤交换性酸的结果偏高.因此,对于高有机质质量分数的酸性土壤,不宜采用BaCl_2-TEA提取法测定其交换性酸含量.但在KCl淋溶法中,由于K~+对Al~(3+)的交换能力较弱,使得该方法测得的土壤交换性酸含量偏低.因此,可乘以1.5左右的校正系数,以便能真实地反映出土壤交换性酸含量.     

The Effects of the Chemical Components of Soil Salinity on Electrical Conductivity in the Region of the Delta Oasis of Weigan and Kuqa Rivers, China

In order to assess the effects of chemical properties of soil salinity on electrical conductivity of 1：5 soil/water extract （EC1：5）, the study focused on revealing the main chemical factors contributing to EC of soil extracts and their relative importance. The relationship between EC1：5 and the chemical properties of soil salinity in the delta oasis of Weigan and Kuqa rivers, China, were studied using path coefficient analysis, a path analysis method. We studied each key element affecting EC1：5 either directly or indirectly. The results obtained show that the salt content, total dissolved solids （TDS）, and the sum of the sodium ion concentration and the kalium ion concentration are the most influential factors on 1：5 soil/ water extract （EC1：5） in the 0-10 cm and the 30-50 cm soil layer. The results show that the sequence of direct path coefficients in the 0-10 cm and the 30-50 cm soil layers on soil conductivity is TDS→Na^＋ ＋ K^＋→Salt content→Ca^2＋→Cl-→the sodium dianion ratio （SDR）→pH→ SO4^2-→HCO3^-→Mg^2＋→the soluble sodium percentage （SSP） sodium absorption ratio （SAR） and TDS→Salt content→Na^＋ ＋ K^＋→Ca^2＋→SDR→Mg^2＋→HCO3^-→SSP→pH→SO4^2-→SAR→Cl^-. The salt content, chlorine ion, and SAR are the main factors affecting 1：5 soil/water extract （EC1：5） in the 10-30 centimeter soil layer. The order of direct path coefficients result is as follows： Salt content→Cl^-→SAR→SSP→TDS→Ca^2＋→Mg^2＋= SO4^2-→HCO3^-→pH→SDR→Na^- ＋ K^＋. Moreover, the effects of HCO3^-, pH were very weak. Though the direct path coefficients between EC1：5 and SAR, SO4^2- and Ca^2＋ were not high, influence of other chemical factors caused the coefficients to increase, making the summation of their direct and indirect path coefficients relatively high. The models of the different soil layers were structured separately. Evidences showed that multiple regression relations between EC1：5 and most of the primary factors had sound reliability and very good accuracy. The research results can serve as a reference to the scientific management amelioration and utilization of saline in the Delta Oasis of Weigan and Kuqa rivers.     

EM38 for volumetric soil water content estimation in the root-zone of deep vertosol soils

Electromagnetic induction sensors, such as EM38, are used widely for monitoring and mapping soil attributes via the apparent electrical conductivity (EC_a) of the soil. The sensor response is the depth-integrated combination of the depth-response function of the EM38 and ‘local’ electrical conductivity (EC_az) at depth. In deep, Vertosol soils, assuming the instrument depth-response function is not perturbed by the soil and where volumetric moisture content at depth (θ_v(z)) dominates EC_az, EM38 should be capable of predicting average moisture content without recourse to mathematically complicated, and unstable profile inversion processes. Firstly a multi-height EM38 experiment was conducted over deep Vertosol soils to confirm the veracity of the EM38 depth-response function and test the concomitant hypothesis of the EM38 response being an integrated (i.e. additive) combination of depth-response function and θ_v(z). Secondly, depth profiles of moisture content were used to calibrate the EM38 to infer average θ_v(z) within the ‘root-zone’ of crop plants—here taken to be surface—0.8 m and surface—1.2 m. EM38 calibration was performed using soil samples acquired from both extracted cores and excavated pits. Mathematical summation of measured θ_v(z) from sectioned cores and the known depth-response function of the EM38 was found to explain 99% and 97% of the variance in measured EC_a for horizontal and vertical dipole configurations at multiple sensor heights above the ground. Average θ_v from surface to 0.8 m () and surface to 1.2 m () explained only 37% and 46% of the variance in on-ground EC_a for vertical dipole configuration measurements compared to 55% and 56% of the variance for horizontal dipole configuration. In a separate validation experiment, the shape of the vertical moisture profile proved highly influential in determining the ability of the calibration equations to infer underlying average moisture content, especially where the depth profile shapes differed between sensor calibration and subsequent field validation (for example following rainfall or irrigation).